code: def _rl_estimator(
      sagemaker_session,
      toolkit=RLToolkit.COACH,
      toolkit_version=RLEstimator.COACH_LATEST_VERSION_MXNET,
      framework=RLFramework.MXNET,
      instance_type=None,
      base_job_name=None,
      **kwargs,
  ):
      return RLEstimator(
          entry_point=SCRIPT_PATH,
          toolkit=toolkit,
          toolkit_version=toolkit_version,
          framework=framework,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_count=INSTANCE_COUNT,
          instance_type=instance_type or INSTANCE_TYPE,
          base_job_name=base_job_name,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import pytest
  from mock import MagicMock, Mock, patch
  from sagemaker.mxnet import MXNetModel, MXNetPredictor
  from sagemaker.rl import RLEstimator, RLFramework, RLToolkit, TOOLKIT_FRAMEWORK_VERSION_MAP
  from sagemaker.session_settings import SessionSettings
  from sagemaker.tensorflow import TensorFlowModel, TensorFlowPredictor
label_desc: Use image node_id 3 for calling a global function with example usage: _rl_estimator(sagemaker_session, toolkit, toolkit_version, framework, instance_type, base_job_name) and returns: RLEstimator